REALY: Rethinking the Evaluation of 3D Face Reconstruction
| Autor: | Chai, Zenghao, Zhang, Haoxian, Ren, Jing, Kang, Di, Xu, Zhengzhuo, Zhe, Xuefei, Yuan, Chun, Bao, Linchao |
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| Rok vydání: | 2022 |
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| Druh dokumentu: | Working Paper |
| Popis: | The evaluation of 3D face reconstruction results typically relies on a rigid shape alignment between the estimated 3D model and the ground-truth scan. We observe that aligning two shapes with different reference points can largely affect the evaluation results. This poses difficulties for precisely diagnosing and improving a 3D face reconstruction method. In this paper, we propose a novel evaluation approach with a new benchmark REALY, consists of 100 globally aligned face scans with accurate facial keypoints, high-quality region masks, and topology-consistent meshes. Our approach performs region-wise shape alignment and leads to more accurate, bidirectional correspondences during computing the shape errors. The fine-grained, region-wise evaluation results provide us detailed understandings about the performance of state-of-the-art 3D face reconstruction methods. For example, our experiments on single-image based reconstruction methods reveal that DECA performs the best on nose regions, while GANFit performs better on cheek regions. Besides, a new and high-quality 3DMM basis, HIFI3D++, is further derived using the same procedure as we construct REALY to align and retopologize several 3D face datasets. We will release REALY, HIFI3D++, and our new evaluation pipeline at https://realy3dface.com. Comment: Accepted to ECCV 2022, camera-ready version; Project page: https://realy3dface.com; Code: https://github.com/czh-98/REALY |
| Databáze: | arXiv |
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